AWS Quick sight

Key Responsibilities

AWS QuickSight PixelPerfect Reporting

Design, develop, and deploy Pixel Perfect reports and interactive dashboards using AWS QuickSight.

Ensure accurate, visually appealing, and business relevant reporting for stakeholders.

Customize and optimize QuickSight reports with advanced features like calculated fields, filters, and visualizations to meet business needs.

Data Engineering and Data Integration

Develop and maintain scalable data pipelines using Google Cloud BigQuery, DataProc, DataFlow, and DataFusion.

Design and implement ETL processes to extract, transform, and load data from various sources into data lakes and warehouses.

Integrate data from on premise systems and other cloud platforms (AWS, GCP) into BigQuery and other data storage solutions.

BigQuery

Develop and manage BigQuery queries and data models to support reporting, analytics, and machine learning workloads.

Work with large datasets, ensuring high performance and cost efficiency in data processing and querying.

Leverage BigQuery ML and other BigQuery features for advanced analytics and reporting.

DataFlow, DataProc, and DataFusion

Build and manage cloud native data pipelines using DataFlow and DataProc for stream and batch processing.

Design, implement, and maintain data transformations using DataFusion to integrate and manage diverse datasets from different sources.

Leverage cloud based tools for automating data workflows and ensuring data consistency across systems.

Collaboration with Business Teams

Collaborate with business analysts, product owners, and data scientists to understand data requirements and translate them into actionable insights and reports.

Provide training and support to business users on using AWS QuickSight for self-service analytics.

Data Security and Governance

Ensure that all data processing and reporting comply with security and governance policies.

Manage permissions, roles, and data access controls in AWS and Google Cloud environments.

Implement and maintain data quality checks and monitoring for all data pipelines.

GCP Data Services

Experience with DataProc, DataFlow, and DataFusion for managing and processing big data in the cloud.

Strong knowledge of Google Cloud Platform tools for data engineering and integration.

ETL Development Strong background in ETL development using cloudnative technologies like DataFlow, DataProc, and other data integration tools.

SQL & Data Modeling Advanced SQL skills for querying and optimizing large datasets in BigQuery and other relational databases.

Business Intelligence Hands on experience with creating dashboards, reports, and visualizations in AWS QuickSight or similar BI tools Tableau, Power BI

Cloud Infrastructure Familiarity with AWS and GCP cloud environments, data storage, and orchestration services Google Cloud Storage, AWS S3

Data Pipeline Automation Experience automating data pipelines, error handling, and monitoring for end to end data flow.

Data Governance Knowledge of data governance principles, data security, and ensuring compliance in cloud based data engineering.

Preferred Skills

Experience with AWS Glue or Google Cloud Dataflow for ETL processing.

Familiarity with Python, Java, or Scala for writing custom data processing logic.

Knowledge of Machine Learning concepts and tools BigQuery ML, TensorFlow for integrating analytics into data pipelines.

Familiarity with Airflow or similar orchestration tools.